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Markov Chain Monte Carlo

142 directly classified papers

Papers per year

Papers

Nonlinear MCMC for Bayesian Machine Learning NIPS 2022
Training stochastic stabilized supralinear networks by dynamics-neutral growth NIPS 2022
FedPop: A Bayesian Approach for Personalised Federated Learning NIPS 2022
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space NIPS 2022
Continuously Tempered PDMP samplers NIPS 2022
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients NIPS 2022
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network NIPS 2022
Langevin Autoencoders for Learning Deep Latent Variable Models NIPS 2022
Fixed-Distance Hamiltonian Monte Carlo NIPS 2022
Local-Global MCMC kernels: the best of both worlds NIPS 2022
Efficient Sampling on Riemannian Manifolds via Langevin MCMC NIPS 2022
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NIPS 2022
Bayesian Clustering of Neural Spiking Activity Using a Mixture of Dynamic Poisson Factor Analyzers NIPS 2022
Constrained Langevin Algorithms with L-mixing External Random Variables NIPS 2022
Slice Sampling Reparameterization Gradients NIPS 2021
Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms NIPS 2021
De-randomizing MCMC dynamics with the diffusion Stein operator NIPS 2021
Learning Deep Latent Variable Models by Short-Run MCMC Inference With Optimal Transport Correction CVPR 2021
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions NIPS 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise NIPS 2021
Rao-Blackwellised parallel MCMC AISTATS 2021
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds NIPS 2021
Optimal Underdamped Langevin MCMC Method NIPS 2021
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev NIPS 2020
Smooth Shells: Multi-Scale Shape Registration With Functional Maps CVPR 2020